Low-Overhead General-Purpose Near-Data Processing in CXL Memory Expanders

Emerging Compute Express Link (CXL) enables cost-efficient memory expansion beyond the local DRAM of processors. While its CXL.mem protocol provides minimal latency overhead through an optimized protocol stack, frequent CXL memory accesses can result in significant slowdowns for memory-bound applications whether they are latency-sensitive or bandwidth-intensive. The near-data processing (NDP) in the CXL controller promises to overcome such limitations of passive CXL memory. However, prior work on NDP in CXL memory proposes application-specific units that are not suitable for practical CXL memory-based systems that should support various applications. On the other hand, existing CPU or GPU cores are not cost-effective for NDP because they are not optimized for memory-bound applications. In addition, the communication between the host processor and CXL controller for NDP offloading should achieve low latency, but existing CXL.io/PCIe-based mechanisms incur <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu\mathbf{s}-\mathbf{scale}$</tex> latency and are not suitable for fine-grained NDP.

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Publication Details

Published
2024-11-02
DOI
https://doi.org/10.1109/micro61859.2024.00051
Citations
16
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
7.82

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article

Low-Overhead General-Purpose Near-Data Processing in CXL Memory Expanders

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Low-Overhead General-Purpose Near-Data Processing in CXL Memory Expanders

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article en
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Abstract

Emerging Compute Express Link (CXL) enables cost-efficient memory expansion beyond the local DRAM of processors. While its CXL.mem protocol provides minimal latency overhead through an optimized protocol stack, frequent CXL memory accesses can result in significant slowdowns for memory-bound applications whether they are latency-sensitive or bandwidth-intensive. The near-data processing (NDP) in the CXL controller promises to overcome such limitations of passive CXL memory. However, prior work on NDP in CXL memory proposes application-specific units that are not suitable for practical CXL memory-based systems that should support various applications. On the other hand, existing CPU or GPU cores are not cost-effective for NDP because they are not optimized for memory-bound applications. In addition, the communication between the host processor and CXL controller for NDP offloading should achieve low latency, but existing CXL.io/PCIe-based mechanisms incur <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mu\mathbf{s}-\mathbf{scale}$</tex> latency and are not suitable for fine-grained NDP.

Pohang University of Science and Technology (KR), Seoul National University (KR), Korea Post (KR), SK Group (Japan) (JP), Artificial Intelligence in Medicine (Canada) (CA)
Ministry of Science and ICT, South Korea
Affordable and clean energy
Openalex Percentile: Top 2%
Parallel Computing and Optimization Techniques
7.82
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